J. Nolte, The human brain: an introduction to its functional anatomy, 2002.

D. S. Bassett and M. S. Gazzaniga, Understanding complexity in the human brain, Trends in Cognitive Sciences, vol.15, issue.5, pp.200-209, 2011.
DOI : 10.1016/j.tics.2011.03.006

B. B. Biswal, M. Mennes, X. Zuo, S. Gohel, C. Kelly et al., Toward discovery science of human brain function, Proceedings of the National Academy of Sciences, pp.4734-4739
DOI : 10.1038/nature08494

Y. Wang, Special issue on cognitive computing, on abstract intelligence, International Journal of Software Science and Computational Intelligence, vol.1, issue.3, 2009.

J. O. Kephart and D. M. Chess, The vision of autonomic computing, Computer, vol.36, issue.1, pp.41-50, 2003.
DOI : 10.1109/MC.2003.1160055

J. Kelly, I. , and S. Hamm, Smart Machines: IBM's Watson and the Era of Cognitive Computing, 2013.
DOI : 10.7312/kell16856

J. E. Kelly, Computing, cognition and the future of knowing, Whitepaper, IBM Reseach, 2015.

. Gartner, Gartner Says 4.9 Billion Connected "Things" Will Be in Use in 2015Online; accessed 15, 2016.

A. Noronha, R. Moriarty, K. O. Connell, and N. Villa, Attaining iot value: How to move from connecting things to capturing insights, p.2014

V. Foteinos, D. Kelaidonis, G. Poulios, P. Vlacheas, V. Stavroulaki et al., Cognitive Management for the Internet of Things: A Framework for Enabling Autonomous Applications, IEEE Vehicular Technology Magazine, vol.8, issue.4, pp.90-99
DOI : 10.1109/MVT.2013.2281657

S. M. Meystre, G. K. Savova, K. C. Kipper-schuler, and J. F. Hurdle, Extracting information from textual documents in the electronic health record: a review of recent research, Yearb Med Inform, vol.35, pp.128-172, 2008.

D. S. Wishart, C. Knox, A. C. Guo, S. Shrivastava, M. Hassanali et al., DrugBank: a comprehensive resource for in silico drug discovery and exploration, Nucleic Acids Research, vol.34, issue.90001, pp.668-672, 2006.
DOI : 10.1093/nar/gkj067

M. A. Nicolelis, Mind in Motion, Scientific American, vol.307, issue.3, pp.58-63
DOI : 10.1038/scientificamerican0912-58

A. Gluhak, S. Krco, M. Nati, D. Pfisterer, N. Mitton et al., A survey on facilities for experimental internet of things research, IEEE Communications Magazine, vol.49, issue.11, pp.58-67, 2011.
DOI : 10.1109/MCOM.2011.6069710
URL : https://hal.archives-ouvertes.fr/inria-00630092

R. De-lemos, H. Giese, H. A. Müller, M. Shaw, J. Andersson et al., Software Engineering for Self-Adaptive Systems: A Second Research Roadmap, Software Engineering for Self-Adaptive Systems II, pp.1-32
DOI : 10.1145/1808984.1808994
URL : https://hal.archives-ouvertes.fr/inria-00638157

F. Belleau, M. Nolin, N. Tourigny, P. Rigault, and J. Morissette, Bio2RDF: Towards a mashup to build bioinformatics knowledge systems, Journal of Biomedical Informatics, vol.41, issue.5, pp.706-716, 2008.
DOI : 10.1016/j.jbi.2008.03.004
URL : https://doi.org/10.1016/j.jbi.2008.03.004

J. Gantz and D. , Extracting value from chaos, IDC iview, vol.1142, issue.34, pp.1-12, 2011.

H. Banaee, M. U. Ahmed, and A. Loutfi, Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges, Sensors, vol.56, issue.12, pp.17472-17500
DOI : 10.1016/j.artmed.2012.09.002

S. Redmond, N. Lovell, G. Yang, A. Horsch, P. Lukowicz et al., What Does Big Data Mean for Wearable Sensor Systems?, IMIA Yearbook, vol.9, issue.1, pp.135-2014
DOI : 10.15265/IY-2014-0019
URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4287062/pdf

D. Che, M. Safran, and Z. Peng, From Big Data to Big Data Mining: Challenges, Issues, and Opportunities, International Conference on Database References 149
DOI : 10.1007/978-3-642-40270-8_1

M. B. Alaya, Self-management, and Scalability for Machine-to-Machine Systems
DOI : 10.1002/cpe.3168
URL : https://hal.archives-ouvertes.fr/tel-01354068

E. Mezghani, R. B. Halima, and K. Drira, DRAAS: Dynamically Reconfigurable Architecture for Autonomic Services, Web Services Foundations, pp.483-505
DOI : 10.1007/978-1-4614-7518-7_19
URL : https://hal.archives-ouvertes.fr/hal-00675439

M. C. Huebscher and J. A. Mccann, A survey of autonomic computing???degrees, models, and applications, ACM Computing Surveys, vol.40, issue.3, p.7, 2008.
DOI : 10.1145/1380584.1380585

C. Klein, R. Schmid, C. Leuxner, W. Sitou, and B. Spanfelner, A Survey of Context Adaptation in Autonomic Computing, Fourth International Conference on Autonomic and Autonomous Systems (ICAS'08), pp.106-111, 2008.
DOI : 10.1109/ICAS.2008.23

A. G. Ganek and T. A. Corbi, The dawning of the autonomic computing era, IBM Systems Journal, vol.42, issue.1, pp.5-18, 2003.
DOI : 10.1147/sj.421.0005

M. B. Alaya, Y. Banouar, T. Monteil, C. Chassot, and K. Drira, OM2M: Extensible ETSI-compliant M2M Service Platform with Self-configuration Capability, Procedia Computer Science, vol.32, pp.1079-1086
DOI : 10.1016/j.procs.2014.05.536
URL : https://doi.org/10.1016/j.procs.2014.05.536

D. Weyns, B. Schmerl, V. Grassi, S. Malek, R. Mirandola et al., On Patterns for Decentralized Control in Self-Adaptive Systems, Software Engineering for Self-Adaptive Systems II, pp.76-107
DOI : 10.1145/1808984.1808994

T. Li, Y. Liu, Y. Tian, S. Shen, and W. Mao, A Storage Solution for Massive IoT Data Based on NoSQL, 2012 IEEE International Conference on Green Computing and Communications, pp.50-57
DOI : 10.1109/GreenCom.2012.18

D. G. Páez, F. Aparicio, M. De-buenaga, and J. R. Ascanio, Big Data and IoT for Chronic Patients Monitoring, International Conference on Ubiquitous Computing and Ambient Intelligence, pp.416-423
DOI : 10.1007/978-3-319-13102-3_68

L. Wang and R. Ranjan, Processing Distributed Internet of Things Data in Clouds, IEEE Cloud Computing, vol.2, issue.1, pp.76-80
DOI : 10.1109/MCC.2015.14

A. Al-fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications, IEEE Communications Surveys & Tutorials, vol.17, issue.4, pp.2347-2376
DOI : 10.1109/COMST.2015.2444095

C. Hochreiner, S. Schulte, S. Dustdar, and F. Lecue, Elastic Stream Processing for Distributed Environments, IEEE Internet Computing, vol.19, issue.6, pp.54-59, 2015.
DOI : 10.1109/MIC.2015.118

J. Soldatos, N. Kefalakis, M. Hauswirth, M. Serrano, J. Calbimonte et al., OpenIoT: Open Source Internet-of-Things in the Cloud, Interoperability and Open-Source Solutions for the Internet of Things, pp.13-25
DOI : 10.1007/978-3-319-16546-2_3

A. Bassi, M. Bauer, M. Fiedler, T. Kramp, R. Van-kranenburg et al., Enabling things to talk Designing IoT Solutions With the IoT Architectural Reference Model, pp.163-211

N. Lasierra, A. Alesanco, S. Guillén, and J. Garcia, A three stage ontology-driven solution to provide personalized care to chronic patients at home, Journal of Biomedical Informatics, vol.46, issue.3, pp.516-529, 2013.
DOI : 10.1016/j.jbi.2013.03.006

M. B. Alaya, S. Medjiah, T. Monteil, and K. Drira, Toward semantic interoperability in oneM2M architecture, IEEE Communications Magazine, vol.53, issue.12, pp.35-41
DOI : 10.1109/MCOM.2015.7355582

J. Kim, J. Kim, D. Lee, and K. Chung, Ontology driven interactive healthcare with wearable sensors, Multimedia Tools and Applications, pp.827-841, 2014.
DOI : 10.1186/1471-2105-12-1

M. Jarrar and R. Meersman, Scalability and knowledge reusability in ontology modeling, p.9, 2002.

A. W. Brown, Model driven architecture: Principles and practice Software and Systems Modeling, pp.314-327, 2004.
DOI : 10.1007/s10270-004-0061-2

R. France and B. Rumpe, Model-driven Development of Complex Software: A Research Roadmap, Future of Software Engineering (FOSE '07), pp.37-54, 2007.
DOI : 10.1109/FOSE.2007.14
URL : https://hal.archives-ouvertes.fr/inria-00511368

A. Alnusair and T. Zhao, Towards a model-driven approach for reverse engineering design patterns, Proceedings of the 2nd International Workshop on Transforming and Weaving Ontologies in MDE, 2009.

G. Hohpe and B. Woolf, Enterprise integration patterns: Designing, building , and deploying messaging solutions, 2004.

W. M. Van-der-aalst, A. H. Ter-hofstede, B. Kiepuszewski, and A. P. Barros, Workflow Patterns, Distributed and parallel databases, pp.5-51, 2003.
DOI : 10.1007/11568322_23

E. Gamma, Design patterns: elements of reusable object-oriented software. Pearson Education India, pp.16-47, 1995.

D. Atwood, Bpm process patterns: Repeatable design for bpm process models, 2006.

C. Leymann, F. Fehling, R. Retter, W. Schupeck, and P. Arbitter, Cloud computing patterns, p.2014

C. Vidal, C. Fernández-sánchez, J. Díaz, and J. Pérez, A Model-Driven Engineering Process for Autonomic Sensor-Actuator Networks, International Journal of Distributed Sensor Networks, vol.19, issue.10, pp.18-35, 2015.
DOI : 10.1109/oceanse.2009.5278105
URL : https://doi.org/10.1155/2015/684892

A. Computing, An architectural blueprint for autonomic computing, 2003.

M. Parashar and S. Hariri, Autonomic computing: concepts, infrastructure, and applications, 2006.

S. Dobson, S. Denazis, A. Fernández, D. Gaïti, E. Gelenbe et al., A survey of autonomic communications, ACM Transactions on Autonomous and Adaptive Systems, vol.1, issue.2, pp.223-259, 2006.
DOI : 10.1145/1186778.1186782

P. Dazzi, F. Nidito, and M. Pasquali, New perspectives in autonomic design patterns for stream-classification-systems, Proceedings of the 2007 workshop on Automating service quality Held at the International Conference on Automated Software Engineering (ASE), WRASQ '07, pp.34-37, 2007.
DOI : 10.1145/1314483.1314490

A. J. Ramirez and B. H. Cheng, Design patterns for developing dynamically adaptive systems, Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, SEAMS '10, pp.49-58
DOI : 10.1145/1808984.1808990
URL : http://www.cse.msu.edu/~mckinley/Pubs/files/design-patterns-2010.pdf

B. Solomon, D. Ionescu, M. Litoiu, and M. Mihaescu, Towards a realtime reference architecture for autonomic systems, Proceedings of the 2007 International Workshop on Software Engineering for Adaptive and Self-Managing Systems, pp.10-20, 2007.
DOI : 10.1109/seams.2007.20

V. Mannava and T. Ramesh, Design Pattern for Dynamic Reconfiguration of Component-based Autonomic Computing Systems using RMI, Procedia Technology, vol.6, pp.590-597
DOI : 10.1016/j.protcy.2012.10.071

S. Frey, A. Diaconescu, and I. Demeure, Architectural integration patterns for autonomic management systems, Proceedings of the 9th IEEE International Conference and Workshops on the Engineering of Autonomic and Autonomous Systems, pp.20-25, 2012.

A. Al-shishtawy, V. Vlassov, P. Brand, and S. Haridi, A Design Methodology for Self-Management in Distributed Environments, 2009 International Conference on Computational Science and Engineering, pp.430-436, 2009.
DOI : 10.1109/CSE.2009.301

Y. Abuseta and K. Swesi, Design Patterns for Self Adaptive Systems Engineering, International Journal of Software Engineering & Applications, vol.6, issue.4
DOI : 10.5121/ijsea.2015.6402
URL : http://arxiv.org/abs/1508.01330

F. A. De-oliveira-jr, R. Sharrock, and T. Ledoux, Synchronization of multiple autonomic control loops: Application to cloud computing, International Conference on Coordination Languages and Models, pp.29-43
URL : https://hal.archives-ouvertes.fr/hal-00682914

C. Krupitzer, F. M. Roth, S. Vansyckel, G. Schiele, and C. Becker, A survey on engineering approaches for self-adaptive systems, Pervasive and Mobile Computing, vol.17, pp.184-206
DOI : 10.1016/j.pmcj.2014.09.009

J. Hurwitz, M. Kaufman, and A. Bowles, Cognitive computing and big data analytics, 2015.
DOI : 10.1002/9781119183648

A. Anand and M. Singh, Understanding knowledge management, International Journal of Engineering Science and Technology, vol.3, issue.29, pp.926-939, 2011.

T. H. Davenport and L. Prusak, Working knowledge, Ubiquity, vol.2000, issue.August, 1998.
DOI : 10.1145/347634.348775

J. R. Anderson, Cognitive psychology and its implications WH Freeman/- Times Books, 1990.

J. A. Woods and J. Cortada, The knowledge management yearbook, 2000.

S. M. Jasimuddin, An integration of knowledge transfer and knowledge storage: an holistic approach, Comput Sci Eng, vol.18, issue.1, pp.37-49, 2005.

L. M. Markus, Toward a theory of knowledge reuse: Types of knowledge reuse situations and factors in reuse success, Journal of management information systems, vol.18, issue.1, pp.57-93, 2001.

M. Minsky, A FRAMEWORK FOR REPRESENTING KNOWLEDGE, 1975.
DOI : 10.1016/B978-1-4832-1446-7.50018-2

M. R. Quillian, Word concepts: A theory and simulation of some basic semantic capabilities, Behavioral Science, vol.104, issue.5, pp.410-430, 1967.
DOI : 10.1037/13117-003

F. Van-harmelen, V. Lifschitz, and B. Porter, Handbook of knowledge representation, 2008.

J. F. Sowa, Knowledge representation: logical, philosophical, and computational foundations, 1999.

F. Baader, The description logic handbook: Theory, implementation and applications, 2003.
DOI : 10.1017/CBO9780511711787

I. Jurisica, J. Mylopoulos, and E. Yu, Ontologies for Knowledge Management: An Information Systems Perspective, Knowledge and Information Systems, vol.12, issue.4, pp.380-401, 2004.
DOI : 10.1007/3-540-45581-7_14

T. R. Gruber, Toward principles for the design of ontologies used for knowledge sharing?, International Journal of Human-Computer Studies, vol.43, issue.5-6, pp.907-928, 1995.
DOI : 10.1006/ijhc.1995.1081

I. Horrocks, Ontologies and the semantic web, Communications of the ACM, vol.51, issue.12, pp.58-67, 2008.
DOI : 10.1145/1409360.1409377

M. Compton, P. Barnaghi, L. Bermudez, R. García-castro, O. Corcho et al., The SSN ontology of the W3C semantic sensor network incubator group, Web Semantics: Science, Services and Agents on the World Wide Web, pp.25-32
DOI : 10.1016/j.websem.2012.05.003

M. Gagnon, Ontology-based integration of data sources, " in Information Fusion, 10th International Conference on, pp.1-8, 2007.

N. F. Noy, Semantic integration, ACM SIGMOD Record, vol.33, issue.4, pp.65-70, 2004.
DOI : 10.1145/1041410.1041421

Y. Sure, S. Staab, and R. Studer, Ontology engineering methodology, Handbook on ontologies, pp.135-152, 2009.
DOI : 10.1007/978-3-540-92673-3_6

B. Stadlhofer, P. Salhofer, and A. Durlacher, An overview of ontology engineering methodologies in the context of public administration, Proceedings of the 7th International Conference on Advances in Semantic Processing, pp.36-42

M. Fernández-lópez, Overview of methodologies for building ontologies, 1999.

M. Fernández-lópez, A. Gómez-pérez, and N. Juristo, Methontology: from ontological art towards ontological engineering, 1997.

Y. Sure, S. Staab, and R. Studer, On-To-Knowledge Methodology (OTKM), Handbook on ontologies, pp.117-132, 2004.
DOI : 10.1007/978-3-540-24750-0_6
URL : http://www.aifb.uni-karlsruhe.de/WBS/ysu/./publications/2003_ontohandbook_otkmethodology.pdf

O. Corcho, M. Fernández-lópez, and A. Gómez-pérez, Methodologies, tools and languages for building ontologies. Where is their meeting point?, Data & Knowledge Engineering, vol.46, issue.1, pp.41-64, 2003.
DOI : 10.1016/S0169-023X(02)00195-7
URL : http://www.cs.man.ac.uk/~ocorcho/documents/DKE2003_CorchoEtAl.pdf

K. Kotis and G. A. Vouros, Human-centered ontology engineering: The HCOME methodology, Knowledge and Information Systems, vol.5, issue.(1), pp.109-131, 2006.
DOI : 10.1007/s10115-003-0138-1

M. C. Suárez-figueroa, A. Gomez-perez, and M. Fernandez-lopez, The NeOn Methodology for Ontology Engineering, Ontology engineering in a networked world, pp.9-34
DOI : 10.1007/978-3-642-24794-1_2

M. E. Warkentin, L. Sayeed, and R. Hightower, Virtual Teams versus Face-to-Face Teams: An Exploratory Study of a Web-based Conference System, Decision Sciences, vol.37, issue.2, pp.975-996, 1997.
DOI : 10.1037/0022-3514.53.1.81

K. Yaakub, R. Shaari, S. A. Panatik, and A. Rahman, Towards an understanding of the effect of core self-evaluations and knowledge sharing behaviour, International Journal of Applied Psychology, vol.3, issue.1, pp.13-18

T. Berners-lee, J. Hendler, and O. Lassila, The Semantic Web, Scientific American, vol.284, issue.5, pp.28-37, 2001.
DOI : 10.1038/scientificamerican0501-34

J. Hendler and F. Van-harmelen, Chapter 21 The Semantic Web: Webizing Knowledge Representation, Foundations of Artificial Intelligence, vol.3, pp.821-839, 2008.
DOI : 10.1016/S1574-6526(07)03021-0

C. Bizer and A. Schultz, The berlin sparql benchmark, 2009.
DOI : 10.4018/jswis.2009040101
URL : http://www4.wiwiss.fu-berlin.de/bizer/pub/Bizer-Schultz-Berlin-SPARQL-Benchmark-IJSWIS.pdf

S. Schaffert, F. Bry, J. Baumeister, and M. Kiesel, Semantic Wikis, IEEE Software, vol.25, issue.4, pp.8-11, 2008.
DOI : 10.1109/MS.2008.95

Z. A. Bhatti, S. Baile, and H. M. Yasin, The success of corporate wiki systems, Proceedings of the 7th International Symposium on Wikis and Open Collaboration, WikiSym '11, pp.134-143
DOI : 10.1145/2038558.2038581

B. Hoenderboom and P. Liang, A survey of semantic wikis for requirements engineering, 2009.

F. Bry, S. Schaffert, D. Vrande?i´vrande?i´c, and K. Weiand, Semantic Wikis: Approaches, Applications, and Perspectives, Reasoning Web International Summer School, pp.329-369
DOI : 10.1016/j.is.2011.10.004

M. Krötzsch, D. Vrande?i´vrande?i´c, and M. Völkel, Semantic mediawiki, International semantic web conference, pp.935-942, 2006.

M. Buffa, F. Gandon, G. Ereteo, P. Sander, and C. Faron, SweetWiki: A semantic wiki, Web Semantics: Science, Services and Agents on the World Wide Web, pp.84-97, 2008.
DOI : 10.1016/j.websem.2007.11.003
URL : https://hal.archives-ouvertes.fr/hal-01154473

P. Nbd, Nist big data interoperability framework, 2015.

Y. Kang, I. Park, J. Rhee, and Y. Lee, MongoDB-Based Repository Design for IoT-Generated RFID/Sensor Big Data, IEEE Sensors Journal, vol.16, issue.2, pp.485-497
DOI : 10.1109/JSEN.2015.2483499

K. Kambatla, G. Kollias, V. Kumar, and A. Grama, Trends in big data analytics, Journal of Parallel and Distributed Computing, vol.74, issue.7, pp.2561-2573
DOI : 10.1016/j.jpdc.2014.01.003

K. Grolinger, W. A. Higashino, A. Tiwari, and M. A. Capretz, Data management in cloud environments: NoSQL and NewSQL data stores, Journal of Cloud Computing: Advances, Systems and Applications, p.2013
DOI : 10.1145/1925861.1925869
URL : https://doi.org/10.1186/2192-113x-2-22

R. Hecht and S. Jablonski, Nosql evaluation, International conference on cloud and service computing, pp.336-377, 2011.
DOI : 10.1109/csc.2011.6138544

C. Cecchinel, M. Jimenez, S. Mosser, and M. , An Architecture to Support the Collection of Big Data in the Internet of Things, 2014 IEEE World Congress on Services, pp.442-449
DOI : 10.1109/SERVICES.2014.83
URL : https://hal.archives-ouvertes.fr/hal-01341103

L. Jiang, L. Da-xu, H. Cai, Z. Jiang, F. Bu et al., An IoT-Oriented Data Storage Framework in Cloud Computing Platform, IEEE Transactions on Industrial Informatics, vol.10, issue.2, pp.1443-1451
DOI : 10.1109/TII.2014.2306384

M. Chen, Y. Zhang, Y. Li, M. M. Hassan, and A. Alamri, AIWAC: affective interaction through wearable computing and cloud technology, IEEE Wireless Communications, vol.22, issue.1, pp.20-27
DOI : 10.1109/MWC.2015.7054715

P. A. Prakashbhai and H. M. Pandey, Inference patterns from Big Data using aggregation, filtering and tagging- A survey, 2014 5th International Conference, Confluence The Next Generation Information Technology Summit (Confluence), pp.66-71
DOI : 10.1109/CONFLUENCE.2014.6949238

A. Mcafee, E. Brynjolfsson, T. H. Davenport, D. Patil, and D. Barton, Big data The management revolution, Harvard Bus Rev, vol.90, issue.10, pp.61-67

H. Chen, S. Compton, and O. Hsiao, DiabeticLink: A Health Big Data System for Patient Empowerment and Personalized Healthcare, International Conference on Smart Health, pp.71-83
DOI : 10.1007/978-3-642-39844-5_10

A. O. Driscoll, J. Daugelaite, and R. D. Sleator, ???Big data???, Hadoop and cloud computing in genomics, Journal of Biomedical Informatics, vol.46, issue.5, pp.774-781
DOI : 10.1016/j.jbi.2013.07.001

M. Chen, S. Mao, Y. Zhang, and V. C. Leung, Big data: related technologies, challenges and future prospects, p.2014
DOI : 10.1007/978-3-319-06245-7

I. Anagnostopoulos, S. Zeadally, and E. Exposito, Handling big data: research challenges and future directions, The Journal of Supercomputing, vol.64, issue.8, pp.1494-1516
DOI : 10.1109/TC.2014.2360516

M. Barlow, Real-Time Big Data Analytics: Emerging Architecture, p.2013

M. Almorsy, J. Grundy, and A. S. Ibrahim, Collaboration-Based Cloud Computing Security Management Framework, 2011 IEEE 4th International Conference on Cloud Computing, pp.364-371
DOI : 10.1109/CLOUD.2011.9

M. Armbrust, A. Fox, R. Griffith, A. D. Joseph, R. Katz et al., A view of cloud computing, Communications of the ACM, vol.53, issue.4, pp.50-58, 2010.
DOI : 10.1145/1721654.1721672

P. Mell and T. Grance, The nist definition of cloud computing, 2011.
DOI : 10.6028/NIST.SP.800-145

F. Liu, J. Tong, J. Mao, R. Bohn, J. Messina et al., Nist cloud computing reference architecture, NIST special publication, vol.500, issue.88, pp.292-330, 2011.
DOI : 10.6028/NIST.SP.500-292

J. Zhou, T. Leppänen, E. Harjula, M. Ylianttila, T. Ojala et al., CloudThings: A common architecture for integrating the Internet of Things with Cloud Computing, Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp.651-657
DOI : 10.1109/CSCWD.2013.6581037

A. Botta, W. De-donato, V. Persico, and A. Pescapé, Integration of Cloud computing and Internet of Things: A survey, Future Generation Computer Systems, vol.56, pp.684-700
DOI : 10.1016/j.future.2015.09.021

F. Tao, Y. Cheng, L. Da-xu, L. Zhang, and B. H. Li, Cciot-cmfg: cloud computing and internet of things-based cloud manufacturing service system, IEEE Transactions on Industrial Informatics, vol.10, issue.2, pp.1435-1442

S. Kyriazakos, B. Anggorojati, N. Prasad, C. Vallati, E. Mingozzi et al., BETaaS Platform ??? A Things as a Service Environment for Future M2M Marketplaces, Internet of Things. User-Centric IoT, pp.305-313
DOI : 10.1007/978-3-319-19656-5_43

Z. Zheng, J. Zhu, and M. R. Lyu, Service-generated big data and big dataas-a-service: an overview, 2013 IEEE international congress on Big Data, pp.403-410
DOI : 10.1109/bigdata.congress.2013.60

S. K. Sowe, T. Kimata, M. Dong, and K. Zettsu, Managing Heterogeneous Sensor Data on a Big Data Platform: IoT Services for Data-Intensive Science, 2014 IEEE 38th International Computer Software and Applications Conference Workshops, pp.295-300
DOI : 10.1109/COMPSACW.2014.52

M. Ribeiro, K. Grolinger, and M. A. Capretz, MLaaS: Machine Learning as a Service, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp.896-902, 2015.
DOI : 10.1109/ICMLA.2015.152
URL : http://publish.uwo.ca/%7Ekgroling/papers/MLaaS.pdf

S. Xu and W. Zhang, Knowledge as a service and knowledge breaching, 2005 IEEE International Conference on Services Computing (SCC'05) Vol-1
DOI : 10.1109/SCC.2005.60
URL : http://www.cs.utsa.edu/~shxu/scc05.pdf

R. Abdullah, Z. D. Eri, and A. M. Talib, A model of knowledge management system for facilitating knowledge as a service (KaaS) in cloud computing environment, 2011 International Conference on Research and Innovation in Information Systems, pp.1-4, 2011.
DOI : 10.1109/ICRIIS.2011.6125691

Y. Qirui, Kaas-based intelligent service model in agricultural expert system, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), pp.2678-2680
DOI : 10.1109/CECNet.2012.6201763

S. Kannimuthu, K. Premalatha, and S. Shankar, Investigation of high utility itemset mining in service oriented computing: Deployment of knowledge as a service in E-commerce, 2012 Fourth International Conference on Advanced Computing (ICoAC)
DOI : 10.1109/ICoAC.2012.6416812

N. C. Lino, C. D. Siebra, M. Amaro, and A. Tate, Emergencygrid? planning in convergence environments, pp.56-2012, 2012.

S. Maru, G. G. Koch, M. Stender, D. Clark, L. Gibowski et al., Antidiabetic Drugs and Heart Failure Risk in Patients With Type 2 Diabetes in the U.K. Primary Care Setting, Diabetes Care, vol.28, issue.1, pp.20-26, 2005.
DOI : 10.2337/diacare.28.1.20

N. I. On-drug-abuse and U. S. America, Comorbidity: Addiction and other mental illnesses, 2008.

P. Lalanda, Two complementary patterns to build multi-expert systems, Pattern Languages of Programs, 1997.

P. T. Eugster, P. A. Felber, R. Guerraoui, and A. Kermarrec, The many faces of publish/subscribe, ACM Computing Surveys, vol.35, issue.2, pp.114-131, 2003.
DOI : 10.1145/857076.857078

R. C. Atkinson and R. M. Shiffrin, Human Memory: A Proposed System and its Control Processes, Psychology of learning and motivation, vol.2, pp.89-195, 1968.
DOI : 10.1016/S0079-7421(08)60422-3

E. Tulving, How many memory systems are there?, American Psychologist, vol.40, issue.4, p.385, 1985.
DOI : 10.1037/0003-066X.40.4.385

J. R. Manns, R. O. Hopkins, and L. R. Squire, Semantic Memory and the Human Hippocampus, Neuron, vol.38, issue.1, pp.127-133, 2003.
DOI : 10.1016/S0896-6273(03)00146-6
URL : https://doi.org/10.1016/s0896-6273(03)00146-6

A. Soliman, V. Desanctis, M. Yassin, R. Elalaily, and N. E. Eldarsy, Continuous glucose monitoring system and new era of early diagnosis of diabetes in high risk groups, Indian Journal of Endocrinology and Metabolism, vol.18, issue.3, pp.274-2014
DOI : 10.4103/2230-8210.131130

Y. Liu, G. Tong, W. Tong, L. Lu, and X. Qin, Can body mass index, waist circumference, waist-hip ratio and waist-height ratio predict the presence of multiple metabolic risk factors in Chinese subjects?, BMC Public Health, vol.240, issue.1, 2011.
DOI : 10.1126/science.3287615

M. Compton, C. Henson, L. Lefort, H. Neuhaus, and A. Sheth, A survey of the semantic specification of sensors, Proceedings of the 2nd International Conference on Semantic Sensor Networks, pp.17-32, 2009.

B. Farran, A. M. Channanath, K. Behbehani, and T. A. Thanaraj, Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait???a cohort study, BMJ Open, vol.3, issue.5, pp.2457-2013
DOI : 10.1136/bmjopen-2012-002457

E. Chiauzzi, C. Rodarte, and P. Dasmahapatra, Patient-centered activity monitoring in the self-management of chronic health conditions, BMC Medicine, vol.166, issue.1, p.2015
DOI : 10.1164/ajrccm.166.1.at1102

A. Pantelopoulos and N. G. Bourbakis, A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.40, issue.1, pp.1-12, 2010.
DOI : 10.1109/TSMCC.2009.2032660
URL : http://gigapaper.ir/Articles/Most_Downloaded_Papers_from_all_IEEE_Journals/Systems_Man_and_Cybernetics_C/A_Survey_on_Wearable_Sensor-Based_Systems_for_Health_Monitoring_and_Prognosis-mbc.pdf

S. Patel, H. Park, P. Bonato, L. Chan, and M. Rodgers, A review of wearable sensors and systems with application in rehabilitation, Journal of NeuroEngineering and Rehabilitation, vol.9, issue.1, p.2012
DOI : 10.1016/j.jalz.2008.07.004

S. C. Mukhopadhyay, Wearable Sensors for Human Activity Monitoring: A Review, IEEE Sensors Journal, vol.15, issue.3, pp.1321-1330
DOI : 10.1109/JSEN.2014.2370945

J. Penders, M. Altini, C. Van-hoof, and E. Dy, Wearable Sensors for Healthier Pregnancies, Proceedings of the IEEE, pp.179-191
DOI : 10.1109/JPROC.2014.2387017

M. A. Nicolelis, Opinion: Brain???machine interfaces to restore motor function and probe neural circuits, Nature Reviews Neuroscience, vol.4, issue.5, pp.417-422, 2003.
DOI : 10.1038/nrn1105

K. Pretz, Better Health Care Through Data: How health analytics could contain costs and improve careOnline; accessed 15, 2016.

W. Kim, S. Lim, J. Ahn, J. Nah, and N. Kim, Integration of IEEE 1451 and HL7 Exchanging Information for Patients??? Sensor Data, Journal of Medical Systems, vol.34, issue.6, pp.1033-1041, 2010.
DOI : 10.1007/s10916-009-9322-5

Á. Ruiz-zafra, M. Noguera, and K. Benghazi, Towards a Model-Driven Approach for Sensor Management in Wireless Body Area Networks, International Conference on Internet and Distributed Computing Systems, pp.335-347
DOI : 10.1007/978-3-319-11692-1_29

N. Bui, Internet of things architecture (iot-a), project deliverable d1. 1- sota report on existing integration frameworks/architectures for wsn, rfid and other emerging iot related technology, p.2014

T. J. Nec, S. Meissner, G. Völksen, C. Kleegrewe, and S. Becher, Internetof-things architecture iot-a project deliverable d2

X. Xu, S. Huang, Y. Chen, K. Browny, I. Halilovicy et al., TSAaaS: Time Series Analytics as a Service on IoT, 2014 IEEE International Conference on Web Services, pp.249-256
DOI : 10.1109/ICWS.2014.45

E. Mingozzi, G. Tanganelli, C. Vallati, and V. Gregorio, An open framework for accessing things as a service, Wireless Personal Multimedia Communications (WPMC), 2013 16th International Symposium on, pp.1-5

I. Mendia, Semantics in betaas, 2014.

A. Forkan, I. Khalil, and Z. Tari, CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living, Future Generation Computer Systems, vol.35, issue.87, pp.114-127, 2014.
DOI : 10.1016/j.future.2013.07.009

P. Jiang, J. Winkley, C. Zhao, R. Munnoch, G. Min et al., An Intelligent Information Forwarder for Healthcare Big Data Systems With Distributed Wearable Sensors, IEEE Systems Journal, vol.10, issue.3, pp.1147-1159, 2016.
DOI : 10.1109/JSYST.2014.2308324

K. Grolinger, E. Mezghani, M. A. Capretz, and E. Exposito, Collaborative knowledge as a service applied to the disaster management domain, International Journal of Cloud Computing, vol.4, issue.1, pp.5-27
DOI : 10.1504/IJCC.2015.067706

E. Mezghani, E. Exposito, K. Drira, M. D. Silveira, and C. Pruski, A Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare, Journal of Medical Systems, vol.99, issue.2, pp.1-8
DOI : 10.1016/j.future.2013.07.009

M. Lichman, Uci machine learning repository http://archive. ics. uci. edu/ml. irvine, ca: University of california, school of information and computer science, 2013.

A. Wright and D. F. Sittig, A four-phase model of the evolution of clinical decision support architectures, International Journal of Medical Informatics, vol.77, issue.10, pp.641-649, 2008.
DOI : 10.1016/j.ijmedinf.2008.01.004

E. S. Berner and T. J. Lande, Overview of clinical decision support systems, Clinical decision support systems, pp.3-22, 2007.

P. A. De-clercq, J. A. Blom, H. H. Korsten, and A. Hasman, Approaches for creating computer-interpretable guidelines that facilitate decision support, Artificial Intelligence in Medicine, vol.31, issue.1, pp.1-27, 2004.
DOI : 10.1016/j.artmed.2004.02.003

M. A. Grando, D. Glasspool, and A. Boxwala, Argumentation logic for the flexible enactment of goal-based medical guidelines, Journal of Biomedical Informatics, vol.45, issue.5, pp.938-949, 2012.
DOI : 10.1016/j.jbi.2012.03.005

Z. Huang, X. Lu, and H. Duan, Using Recommendation to Support Adaptive Clinical Pathways, Journal of Medical Systems, vol.12, issue.4, pp.1849-1860, 2012.
DOI : 10.1197/jamia.M1798

A. González-ferrer, A. Ten-teije, J. Fdez-olivares, and K. Milian, Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks, Artificial Intelligence in Medicine, vol.57, issue.2, pp.91-109, 2013.
DOI : 10.1016/j.artmed.2012.08.008

G. Milla-millán, J. Fdez-olivares, I. Sánchez-garzón, D. Prior, and L. Castillo, Knowledge-Driven Adaptive Execution of Care Pathways Based on Continuous Planning Techniques, Process Support and Knowledge Representation in Health Care, pp.42-55
DOI : 10.1007/978-3-642-36438-9_3

I. Sánchez-garzón, G. Milla-millán, and J. Fernández-olivares, Contextaware generation and adaptive execution of daily living care pathways, International Workshop on Ambient Assisted Living, pp.362-370

D. Riaño, F. Real, J. A. López-vallverdú, F. Campana, S. Ercolani et al., An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients, Journal of Biomedical Informatics, vol.45, issue.3, pp.429-446
DOI : 10.1016/j.jbi.2011.12.008

A. Emerencia, L. Van-der-krieke, S. Sytema, N. Petkov, and M. Aiello, Generating personalized advice for schizophrenia patients, Artificial Intelligence in Medicine, vol.58, issue.1, pp.23-36, 2013.
DOI : 10.1016/j.artmed.2013.01.002

D. A. Alexandrou, I. E. Skitsas, and G. N. Mentzas, A Holistic Environment for the Design and Execution of Self-Adaptive Clinical Pathways, IEEE Transactions on Information Technology in Biomedicine, vol.15, issue.1, pp.108-118, 2011.
DOI : 10.1109/TITB.2010.2074205

W. Yao and A. Kumar, CONFlexFlow: Integrating Flexible clinical pathways into clinical decision support systems using context and rules, Decision Support Systems, vol.55, issue.2, pp.499-515, 2013.
DOI : 10.1016/j.dss.2012.10.008

T. Heath and C. Bizer, Linked data: Evolving the web into a global data space Synthesis lectures on the semantic web: theory and technology, pp.1-136, 2011.

A. Jentzsch, O. Hassanzadeh, C. Bizer, B. Andersson, and S. Stephens, Enabling tailored therapeutics with linked data, Proceedings of the 2nd Workshop on Linked Data on the Web (LDOW2009), pp.1-6, 2009.

M. Samwald, A. Jentzsch, C. Bouton, C. S. Kallesøe, E. Willighagen et al., Linked open drug data for pharmaceutical research and development, Journal of Cheminformatics, vol.3, issue.1, p.19, 2011.
DOI : 10.1186/1758-2946-2-7
URL : https://jcheminf.springeropen.com/track/pdf/10.1186/1758-2946-3-19?site=jcheminf.springeropen.com

S. Ayvaz, J. Horn, O. Hassanzadeh, Q. Zhu, J. Stan et al., Toward a complete dataset of drug???drug interaction information from publicly available sources, Journal of Biomedical Informatics, vol.55, pp.206-217
DOI : 10.1016/j.jbi.2015.04.006

A. Callahan, J. Cruz-toledo, and M. Dumontier, Ontology-Based Querying with Bio2RDF???s Linked Open Data, Journal of Biomedical Semantics, vol.4, issue.Suppl 1, pp.1-2013
DOI : 10.1186/2041-1480-4-S1-S1
URL : https://doi.org/10.1186/2041-1480-4-s1-s1

A. Ostankov, F. Röhrbein, U. Waltingern, K. Calzolari, T. Choukri et al., Linkedhealthanswers: Towards linked data-driven question answering for the health care domain, Proceedings of the Ninth International Conference on Language Resources and Evaluation European Language Resources Association (ELRA), pp.2613-2620, 2014.

A. Khalili and B. Sedaghati, Semantic Medical Prescriptions -- Towards Intelligent and Interoperable Medical Prescriptions, 2013 IEEE Seventh International Conference on Semantic Computing, pp.347-354
DOI : 10.1109/ICSC.2013.66
URL : http://svn.aksw.org/papers/2013/ICSC_Pharmer/public.pdf

P. J. O-'connor, J. M. Sperl-hillen, P. E. Johnson, and W. A. Rush, Identification , classification, and frequency of medical errors in outpatient diabetes care, 2005.

N. and L. Beamonte, An ontology-driven architecture for data integration and management in home-based telemonitoring scenarios, p.2012

S. Wang and R. A. Noe, Knowledge sharing: A review and directions for future research, Human Resource Management Review, vol.20, issue.2, pp.115-131, 2010.
DOI : 10.1016/j.hrmr.2009.10.001

R. J. Brachman and H. J. Levesque, The tractability of subsumption in frame-based description languages, AAAI, pp.34-37, 1984.

E. Mezghani, E. Exposito, and K. Drira, A collaborative methodology for tacit knowledge management: Application to scientific research, Future Generation Computer Systems, vol.54, pp.450-455
DOI : 10.1016/j.future.2015.05.007
URL : https://hal.archives-ouvertes.fr/hal-01149983

M. Ghallab, D. Nau, and P. Traverso, Automated planning: theory & practice, 2004.

E. Mezghani, M. Da-silveira, C. Pruski, E. Exposito, and K. Drira, An Ontology-driven Adaptive System for the Patient Treatment Management, Proceedings of the 28th International Conference on Software Engineering and Knowledge Engineering, p.165
DOI : 10.18293/SEKE2016-155

S. E. Inzucchi, R. M. Bergenstal, J. B. Buse, M. Diamant, E. Ferrannini et al., Management of Hyperglycemia in Type 2 Diabetes: A Patient-Centered Approach: Position Statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD), Diabetes Care, vol.35, issue.6, pp.1364-1379
DOI : 10.2337/dc12-0413

A. Baker, Book: Crossing the Quality Chasm: A New Health System for the 21st Century, BMJ, vol.323, issue.7322, p.1192, 2001.
DOI : 10.1136/bmj.323.7322.1192

R. E. Say and R. Thomson, The importance of patient preferences in treatment decisions--challenges for doctors, BMJ, vol.327, issue.7414, pp.542-545, 2003.
DOI : 10.1136/bmj.327.7414.542

S. W. Lahiri, Management of Type 2 Diabetes: What Is the Next Step After Metformin?, Clinical Diabetes, vol.30, issue.2, pp.72-75
DOI : 10.2337/diaclin.30.2.72